Dataset-1: Drone Remote Controller RF Signal Dataset

Publications Using This Dataset

• B. Gündoğan, H. Ergezer, “Comprehensive Comparison of Various ML Algorithms for RF Fingerprints Classification,” 31st Signal Processing and Communications Applications Conference (SIU 2023), July 2023.
• R. Akter, S. V. Doan, A. Zainudin, D.-S. Kim, “Sparsely Connected Low-Complexity CNN for Unmanned Vehicles Detection (RFDNet),” IEEE Transactions on Vehicular Technology, October 2024.
• Shah et al., “Robust RF Fingerprinting With Signal Denoising and Stacked Multivariate Ensemble Learning,” IEEE Access, September 2025.
• N. Quadar, A. Chehri, B. Debaque, “Feature Learning and Continual Adaptation for Robust RF Fingerprinting in UAV Networks,” Conference paper 2026, May 2026.
• O. O. Medaiyese, M. Ezuma, A. P. Lauf, A. A. Adeniran, “Hierarchical Learning Framework for UAV Detection and Identification,” IEEE Journal of Radio Frequency Identification (IEEE J-RFID), September 2022.
• Kaushik et al., “Entropy-Based Detection Approach for Micro-UAV and Classification Using ML,” 2022 IEEE National Aerospace and Electronics Conference (NAECON), August 2022.
• Q. Chen, W. Wu, W. Luo, “Lossless Compression of Sensor Signals Using an Untrained Multi-Channel RNN Predictor,” Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor, November 2021.
• N. V. Bac, H. V. Phuc, D. V. Sang, H. X. Tinh, T. D. Linh, “Deep-Learning CNN + CFAR for RF-Signature-Based Drone Recognition Under Noise,” Application of CNN deep learning model and CFAR filtering technique for RF-based drone signal classification in noisy conditions, June 2024.
• M. Rabie, C. Panagamuwa, K. G. Kyriakopoulos, “ELC: Evidential Lifelong Classifier for Uncertainty-Aware Radar Pulse Classification,” arXiv preprint arXiv:2604.06958, April 2026.
• T. Delleji, F. Slimeni, “RF-YOLO: A Modified YOLO Model for UAV Detection and Classification Using RF Spectrogram Images,” Telecommunication Systems, February 2025.
• L. H. Kirakosyan, M. V. Navoyan, V. G. Melkonyan, A. S. Sardaryan, S. S. Sargsyan, “Real-Time Radio Signal Classification Based on Spectrograms,” Programming and Computer Software, January 2026.
• K. Bremnes, R. Moen, S. R. Yeduri, R. R. Yakkati, L. R. Cenkeramaddi, “Classification of UAVs Utilizing Fixed Boundary Empirical Wavelet Sub-bands of RF Fingerprints and Deep CNN,” IEEE Sensors Journal, November 2022.
• R. R. Yakkati, A. Gade, B. H. Koduru, B. Pardhasaradhi, L. R. Cenkeramaddi, “Classification of UAVs Using Time-Frequency Analysis of Remote Control Signals and CNN,” 2022 IEEE iSES, December 2022.
• M. H. Rahman, M. A. Aziz, R. Tabassum, M. A. S. Sejan, J. I. Baik, H. K. Song, “Improved Drone Classification and Detection Using RF: A Cascaded Deep Learning Approach,” 2024 15th ICTC, October 2024.
• M. U. Zahid, M. D. Nisar, A. Fazil, J. Ryu, M. H. Shah, “Composite Ensemble Learning Framework for Passive Drone RF Fingerprinting in 6G Networks,” 2024, August 2024.
• A. Singh, V. Sharma, K. Rawat, “Classification of RF Fingerprint Signals from UAV Controller Using ML Techniques,” 2023 IEEE MAPCON, December 2023.
• Jain et al., “Robust Drone Identification via RF Spectrogram Analysis Using RobustSpecNet,” International Conference on Intelligent Systems, September 2026.
• Yiğit et al., “RF Fingerprinting and Classification of 15 Different UAV Remote Controllers via Raw Signal Analysis with 1D-CNN,” 18th International Conference on Electronics, July 2026.
• I. Kaya, B. B. Atalay, K. Yiğit, I. I. Ibrahim, S. M. Bostan, G. Aydemir, M. B. Tabakcioglu, “RF Fingerprint-Based Drone Controller Classification Using Feature Engineering and ML,” 18th International Conference on Electronics, July 2026.
• Wang et al., “Comparative Analysis of Experimental Methodology in RF-Based Drone Detection and Classification Datasets,” IEEE 2025, September 2025.
• M. Ezuma, F. Erden, K. Anjinappa, O. Ozdemir, I. Guvenc, “Detection and Classification of UAVs Using RF Fingerprints in the Presence of Interference,” IEEE Open J. Communication Society, January 2020.